Open Access
ARTICLE
Visual Object Detection and Tracking Using Analytical Learning Approach of Validity Level
Yong‐Hwan Lee, Hyochang Ahn, Hyo‐Beom Ahn, Sun‐Young Lee
Dept. of Digital Contents, Wonkwang University, Iksan, Jeonbuk, Korea
Far East University, Eumseong, Chungbuk, Korea
College of Engineering, Kongju National University, Cheonan, Chungnam, Korea
Dept. of Information Security Engineering, Soonchunhyang University, Asan-si, Chungnam, Korea
* Corresponding Author: Sun‐Young Lee,
Intelligent Automation & Soft Computing 2019, 25(1), 205-215. https://doi.org/10.31209/2018.100000056
Abstract
Object tracking plays an important role in many vision applications. This paper
proposes a novel and robust object detection and tracking method to localize
and track a visual object in video stream. The proposed method is consisted of
three modules; object detection, tracking and learning. Detection module finds
and localizes all apparent objects, corrects the tracker if necessary. Tracking
module follows the interest object by every frame of sequences. Learning
module estimates a detecting error, and updates its value of credibility level.
With a validity level where the tracking is failed on tracing the learned object,
detection module finds again the desired object. The experimental results show
that the proposed approach is more robust in appearance changes, viewpoint
changes, and rotation of the object, compared to the traditional method. The
proposed method can track the interest object accurately in various
environments.
Keywords
Cite This Article
Y. Lee, H. Ahn, H. Ahn and S. Lee, "Visual object detection and tracking using analytical learning approach of validity level,"
Intelligent Automation & Soft Computing, vol. 25, no.1, pp. 205–215, 2019.